ACVAE: A novel self-adversarial variational auto-encoder combined with contrast learning for time series anomaly detection.

Deep generative models have advantages in modeling complex time series and are widely used in anomaly detection. Nevertheless, the existing deep generative approaches mainly concentrate on the investigation of models' reconstruction capability rather than customizing a model suitable for anomaly detection. Meanwhile, VAE-based models suffer from posterior collapse, which can lead to a series of undesirable consequences, such as high false positive rate etc. Based on these considerations, in this paper, we propose a novel self-adversarial variational auto-encoder combined with contrast learning, short for ACVAE, to address these challenges. ACVAE consist of three parts 〈T,E,G〉, wherein the transformation network T is employed to generate abnormal latent representations similar to those normal latent representations encoded by the encoder E, and the decoder G is used to distinguish the two representations. In the framework of this model, the normal reconstructions are considered as positive samples and abnormal reconstructions as negative samples, and the contrast learning is executed on the part E to measure the similarities between inputs and positive samples, dissimilarities between inputs and negative samples. Thus, an improved objective function is proposed by integrating two novel regularizers, one refers to adversarial mechanism and the other involves contrast learning, in which the encoder E and decoder G hold the capability to distinguish, and decoder G is constrained to mitigate the posterior collapse. We perform several experiments on five datasets, whose results show ACVAE outperforms state-of-the-art methods.

Concomitant Ipsilateral Knee Pain Is Associated With Worse Preoperative Functional Status and Short-Term Outcomes After Hip Arthroscopy in Patients With Femoroacetabular Impingement Syndrome.

PURPOSE: (1) To determine the effect of concomitant ipsilateral knee pain (IKP) on short-term outcomes after hip arthroscopic treatment of femoroacetabular impingement syndrome (FAIS) and (2) to determine whether IKP would improve with surgery. METHODS: Data between September 2021 and May 2022 were reviewed. Patients with a diagnosis of FAIS who underwent hip arthroscopy with a minimum of 1-year follow-up were included. The exclusion criteria were prior ipsilateral hip or knee surgery, hip Tönnis grade greater than 1, knee Kellgren-Lawrence grade greater than 2, hip conditions (avascular necrosis, Legg-Calvé-Perthes disease, pigmented villonodular synovitis, osteoid osteoma, synovial chondromatosis, and developmental dysplasia of the hip), and spine diseases. All patients underwent knee magnetic resonance imaging preoperatively. Preoperative and short-term (1-year) patient-reported outcomes were collected, consisting of the Hip Sports Activity Scale score, weekly sports participation, modified Harris Hip Score (mHHS), 12-component International Hip Outcome Tool (iHOT-12) score, and visual analog pain scale (VAS) scores for the hip and the ipsilateral knee. The percentages of patients achieving the minimal clinically important difference and patient acceptable symptom state (PASS) for the mHHS and iHOT-12 score were calculated. Multivariate regression analysis was performed to determine the effect of IKP severity on postoperative outcomes. Subgroup analysis was performed between patients with IKP alleviation and those without IKP alleviation. RESULTS: Among the 107 patients included, 47 presented with preoperative IKP. Compared with patients without IKP, the IKP cohort had comparable knee structural abnormalities (all with P > .05). Still, the IKP cohort showed inferior preoperative values for the mHHS (P = .003), iHOT-12 score (P = .016), hip VAS score (P = .001), and weekly sports participation (P = .039). Postoperatively, the IKP cohort had a lower mHHS (P = .046), lower iHOT-12 score (P = .037), and lower hip VAS score (P = .003) and were less likely to achieve the PASS for the mHHS (P = .021) and iHOT-12 score (P = .049). Patients with higher knee VAS scores were less likely to achieve the PASS for the mHHS (odds ratio, 0.61; P = .023). Within the IKP group, the knee VAS score improved from 2.3 to 1.0 (P < .001). Patients with alleviated IKP showed superior postoperative iHOT-12 scores (P = .038) compared with patients with persistent IKP. CONCLUSIONS: Concomitant IKP at baseline negatively affected preoperative status and short-term clinical outcomes after arthroscopic treatment of FAIS. Patients with IKP were less likely to meet clinical thresholds. Most patients achieved IKP alleviation postoperatively, which was associated with superior clinical outcomes. LEVEL OF EVIDENCE: Level III, retrospective cohort study.

Leveraging History to Predict Infrequent Abnormal Transfers in Distributed Workflows.

Scientific computing heavily relies on data shared by the community, especially in distributed data-intensive applications. This research focuses on predicting slow connections that create bottlenecks in distributed workflows. In this study, we analyze network traffic logs collected between January 2021 and August 2022 at the National Energy Research Scientific Computing Center (NERSC). Based on the observed patterns, we define a set of features primarily based on history for identifying low-performing data transfers. Typically, there are far fewer slow connections on well-maintained networks, which creates difficulty in learning to identify these abnormally slow connections from the normal ones. We devise several stratified sampling techniques to address the class-imbalance challenge and study how they affect the machine learning approaches. Our tests show that a relatively simple technique that undersamples the normal cases to balance the number of samples in two classes (normal and slow) is very effective for model training. This model predicts slow connections with an F1 score of 0.926.

Locating Partial Discharges in Power Transformers with Convolutional Iterative Filtering.

The most common source of transformer failure is in the insulation, and the most prevalent warning signal for insulation weakness is partial discharge (PD). Locating the positions of these partial discharges would help repair the transformer to prevent failures. This work investigates algorithms that could be deployed to locate the position of a PD event using data from ultra-high frequency (UHF) sensors inside the transformer. These algorithms typically proceed in two steps: first determining the signal arrival time, and then locating the position based on time differences. This paper reviews available methods for each task and then propose new algorithms: a convolutional iterative filter with thresholding (CIFT) to determine the signal arrival time and a reference table of travel times to resolve the source location. The effectiveness of these algorithms are tested with a set of laboratory-triggered PD events and two sets of simulated PD events inside transformers in production use. Tests show the new approach provides more accurate locations than the best-known data analysis algorithms, and the difference is particularly large, 3.7X, when the signal sources are far from sensors.

Identification and Safety Evaluation of Ochratoxin A Transformation Product in Rapeseed Oil Refining Process.

Ochratoxin A (OTA) is an important mycotoxin detected in edible oil, and it can be effectively removed by classical edible oil refining processes. However, the fate of OTA in the refining process has not been reported. In this study, we systematically tracked the OTA changes during the oil refining process by fortifying 100 µg/kg OTA in crude rapeseed oil. Results showed that about 10.57%, 88.85%, and 0.58% of OTA were removed during the degumming, deacidification, and decolorization processes. Among them, 16.25% OTA was transferred to the byproducts, including 9.85% in degumming wastewater, 5.68% in soap stock, 0.14% in deacidification wastewater, and 0.58% in the decolorizer; 83.75% OTA was found to transform into the lactone ring opened OTA (OP-OTA) during the deacidification stage, which is attributed to the hydrolysis of the lactone ring of OTA in the alkali refining. The OP-OTA was verified to distribute in the soap stock, and small amounts of OP-OTA could be transferred to deacidified wastewater when the OTA pollution level reached 500 µg/kg in crude rapeseed oil. The OP-OTA exhibited strong toxicity, especially nephrotoxicity, as reflected by the cell viability assay and in silico toxicity. Therefore, the safety of the soap stock processing products from OTA-contaminated rapeseed deserves attention.

Identification of Deoxynivalenol and Degradation Products during Maize Germ Oil Refining Process.

Deoxynivalenol (DON) contamination in germs and germ oil is posing a serious threat to food and feed security. However, the transformation pathway, the distribution of DON, and its degradation products in edible oil refining have not yet been reported in detail. In this work, we systematically explored the variation of DON in maize germ oil during refining and demonstrated that the DON in germ oil can be effectively removed by refining, during which a part of DON was transferred to the wastes, and another section of DON was degraded during degumming and alkali refining. Moreover, the DON degradation product was identified to be norDON B by using the ultraviolet absorption spectrum, high-performance liquid chromatography (HPLC), ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF MS), and nuclear magnetic resonance (NMR) methods, and the degradation product was found to be distributed in waste products during oil refining. This study provides a scientific basis and useful reference for the production of non-mycotoxins edible oil by traditional refining.

[Effects of no-tillage sowing with crop stubbles on seeding emergence and yield of spring wheat in Hexi Oasis Irrigated Area, Northwest China]. / ç•™èŒ¬å è€•æ’­ç§å¯¹æ²³è¥¿ç»¿æ´²çŒåŒºæ˜¥å°éº¦å‡ºè‹—å’Œäº§é‡çš„å½±å“.

We examined the effects of long-term no-tillage sowing with crop stubbles on seedling emergence quality and yield of spring wheat under the three typical spring wheat planting modes of single wheat, wheat-corn intercropping and wheat-soybean intercropping in a long-term field experiment in Hexi oasis irrigated area, aiming to provide theoretical support for the efficient and sustai-nable production of spring wheat. The results showed that, compared with traditional tillage, no-tillage sowing with crop stubbles significantly decreased seedling emergence rate and emergence evenness of spring wheat in wheat-corn intercropping and wheat-soybean intercropping by 3.3%-8.6%, 9.6%-20.5%, 2.9%-8.8%, and 10.7%-61.7%, respectively. Emergence evenness was significantly increased by 14.9% in 2019, while seedling emergence rate was significantly decreased by 4.2% in 2020 under the mode of single wheat compared with traditional tillage. Seedling uniformity of spring wheat seedling stage were reduced under the three typical planting modes, including single wheat, wheat-corn intercropping and wheat-soybean intercropping. Spike number of spring wheat were equal to that of traditional tillage at harvest under the three planting modes of no-tillage sowing with crop stubbles, and the differences were not significant. Effects of seedling emergence rate of spring wheat on yield was weakened by increasing grain number per spike and 1000-grain weight of spring wheat under the three planting modes. Grain yield was significantly increased by 10.3%-12.9% (single wheat), 10.5%-11.9% (wheat-corn intercropping), and 10.3%-22.5% (wheat-soybean intercropping) at harvest, respectively. Our results indicated that no-tillage sowing with crop stubbles was the feasible tillage mode in the production process of spring wheat in Hexi oasis irrigation area with extremely serious farmland wind erosion degradation.

A Gold Growth-Based Plasmonic ELISA for the Sensitive Detection of Fumonisin B1 in Maize.

In this paper, a highly sensitive plasmonic enzyme-linked immunosorbent assay (pELISA) was developed for the naked-eye detection of fumonisin B1 (FB1). Glucose oxidase (GOx) was used as an alternative to horseradish peroxidase as the carrier of the competing antigen. GOx catalyzed the oxidation of glucose to produce hydrogen peroxide, which acted as a reducing agent to reduce Au3+ to Au on the surface of gold seeds (5 nm), This reaction led to a color change in the solution from colorless to purple, which was observable to the naked eye. Various parameters that could influence the detection performance of pELISA were investigated. The developed method exhibited a considerably high sensitivity for FB1 qualitative naked-eye detection, with a visible cut-off limit of 1.25 ng/mL. Moreover, the proposed pELISA showed a good linear range of 0.31-10 ng/mL with a half maximal inhibitory concentration (IC50) of 1.86 ng/mL, which was approximately 13-fold lower than that of a horseradish peroxidase- (HRP)-based conventional ELISA. Meanwhile, the proposed method was highly specific and accurate. In summary, the new pELISA exhibited acceptable accuracy and precision for sensitive naked-eye detection of FB1 in maize samples and can be applied for the detection of other chemical contaminants.

Fluorescence immunoassay based on the enzyme cleaving ss-DNA to regulate the synthesis of histone-ds-poly(AT) templated copper nanoparticles.

Herein, for the first time we report a novel competitive fluorescence immunoassay for the ultrasensitive detection of aflatoxin B1 (AFB1) using histone-ds-poly(AT) templated copper nanoparticles (His-pAT CuNPs) as the fluorescent indicator. In this immunoassay, glucose oxidase (Gox) was used as the carrier of the competing antigen to catalyze the formation of hydrogen peroxide (H2O2) from glucose. H2O2 was converted to a hydroxyl radical using Fenton's reagent, which further regulated the fluorescence signals of His-pAT CuNPs. Owing to the ultrahigh sensitivity of the ss-DNA to the hydroxyl radical, the proposed fluorescence immunoassay exhibited a favorable dynamic linear detection of AFB1 ranging from 0.46 pg mL-1 to 400 pg mL-1 with an good half maximal inhibitory concentration and limit of detection of 6.13 and 0.15 pg mL-1, respectively. The intra- and inter-assay showed that the average recoveries for AFB1 spiked corn samples ranged from 96.87% to 100.73% and 96.67% to 114.92%, respectively. The reliability of this method was further confirmed by adopting ultra-performance liquid chromatography coupled with the fluorescence detector method. In summary, this work offers a novel screening strategy with high sensitivity and robustness for the quantitative detection of mycotoxins or other pollutants for food safety and clinical diagnosis.

Effective Removal of Tetracycline from Aqueous Solution by Organic Acid-Coated Magnetic Nanoparticles.

Self-assembled iron oxide nanocomposites are good magnetic nano-adsorbents that can be prepared using simple methods. Four types of organic acid-functionalised (oleic acid, undecenoic acid, caprylic acid or hexanoic acid) magnetic nanoparticles (MNPs) were synthesised through a one-pot chemisorption method for the removal of tetracycline (TC) from aqueous solution. The undecenoic acid-coated MNPs (UA-MNPs) exhibited the highest adsorption efficiency and can be easily retrieved with a low-gradient magnetic separator (0.4 Tesla) at pH 5.0 aqueous solution. The TC adsorption process on the UA-MNPs followed the Langmuir isotherm and the maximum adsorption capacities increased from 86.96 mg g(-1) to 222.2 mg g(-1) with the increase in temperature from 288 K to 318 K. The kinetics of adsorption fits pseudo-second-order model perfectly with a rate constant, 5.946 g mg(-1) min(-1) at 298 K. The positive values of the enthalpy (AH) and the negative value of the free energy (AG) indicated an endothermic and spontaneous adsorption process of TC on the UA-MNPs. Moreover, the UA-MNPs possessed excellent ability to adsorb the other three major types of TC antibiotics, including chlortetracycline, oxytetracycline and doxycycline.

Sulfonated polystyrene magnetic nanobeads coupled with immunochromatographic strip for clenbuterol determination in pork muscle.

A magnetic solid-phase extraction method (MSPE) was developed to pre-concentrate and cleanup clenbuterol (CLE) from pork muscle. Novel sulfonated polystyrene magnetic nanobeads (spMNBs) were synthesized via a one-pot emulsion copolymerization method by using divinylbenzene, styrene, and sodium styrene sulfonate in the presence of oleic acid-modified and 10-undecylenic acid-modified magnetic ferrofluid. The resulting spMNBs exhibited high adsorption efficiency for CLE and for 10 other common beta-adrenergic agonists, namely, brombuterol, ractopamine, tulobuterol, bambuterol, cimbuterol, mabuterol, clorprenaline, penbutolol, salbutamol, and cimaterol. The adsorption behavior of the spMNBs for CLE was described by the Langmuir equation with a maximum adsorption capacity of 0.41 mg/g. Under the optimized parameters, the extraction of CLE from 0.5 g of pork muscle required 25mg of the spMNBs at a shortened adsorption time (0.5 min). The proposed MSPE was coupled with colloidal gold nanoparticle-based immunochromatographic assay (MSPE-AuNPIA) for the quantitative detection of CLE residue in pork muscle. The limit of detection and limit of quantification for the pork muscle were 0.10 and 0.24 ng/g, respectively. The intra-day and inter-day assay recoveries at three CLE spiked concentrations ranged from 92.5% to 98.1%, with relative standard deviations ranging from 3.2% to 13.0%. The results of MSPE-AuNPIA were confirmed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The CLE values obtained with MSPE-AuNPIA agreed with those obtained with LC-MS/MS.

Coupling visualization and data analysis for knowledge discovery from multi-dimensional scientific data.

Knowledge discovery from large and complex scientific data is a challenging task. With the ability to measure and simulate more processes at increasingly finer spatial and temporal scales, the growing number of data dimensions and data objects presents tremendous challenges for effective data analysis and data exploration methods and tools. The combination and close integration of methods from scientific visualization, information visualization, automated data analysis, and other enabling technologies -such as efficient data management- supports knowledge discovery from multi-dimensional scientific data. This paper surveys two distinct applications in developmental biology and accelerator physics, illustrating the effectiveness of the described approach.